The Full-scale Assembly Simulation Testbed (FAST) Dataset
This provides a new resource for researchers in VR and machine learning, though it is incremental as it primarily addresses a data gap without introducing new methods.
The paper tackles the lack of open datasets in VR research by presenting the FAST dataset, which includes data from 108 participants learning to assemble structures in VR, enabling future studies on user identification, cybersickness, and learning gains.
In recent years, numerous researchers have begun investigating how virtual reality (VR) tracking and interaction data can be used for a variety of machine learning purposes, including user identification, predicting cybersickness, and estimating learning gains. One constraint for this research area is the dearth of open datasets. In this paper, we present a new open dataset captured with our VR-based Full-scale Assembly Simulation Testbed (FAST). This dataset consists of data collected from 108 participants (50 females, 56 males, 2 non-binary) learning how to assemble two distinct full-scale structures in VR. In addition to explaining how the dataset was collected and describing the data included, we discuss how the dataset may be used by future researchers.